Bangalore House Price Predictor 🏠

A machine learning web application that predicts property prices in Bangalore, India. This project uses a Linear Regression model trained on real estate data to estimate house prices based on area (square feet), location, and number of BHK/Bathrooms.

🚀 Features

• Accurate Predictions: Uses a Scikit-learn Linear Regression model optimized via GridSearchCV.
• Dynamic UI: Interactive web interface with a dynamic location dropdown fetched from the model's feature set.
• Data-Driven: Trained on a comprehensive dataset of Bangalore property prices with robust outlier detection and feature engineering.
• Deployment Ready: Includes configuration for deployment on platforms like Heroku.

🛠️ Tech Stack

• Backend: Flask (Python)
• Machine Learning: Scikit-learn, Pandas, NumPy
• Frontend: HTML5, CSS3 (Bootstrap), JavaScript (jQuery)
• Deployment: Gunicorn

📂 Project Structure

• app.py: Main Flask application server.
• model.py: Script for data cleaning, feature engineering, and model training.
• model.ipynb: Jupyter notebook for exploratory data analysis.
• bangalore_home_prices_model.pickle: Trained model artifact.
• columns.json: Mapping of feature names for the model.
• templates/: HTML templates for the web interface.
• static/: CSS/JS and image assets.

⚙️ Installation & Usage

1. Clone the repository

git clone https://github.com/Rahul-Vats20/House-predictor.git
cd House-predictor

2. Install dependencies

It is recommended to use a virtual environment.

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt

3. Run the application

python app.py

Open your browser and navigate to http://127.0.0.1:5000/.

📊 Model Training

If you wish to retrain the model, you can run the model.py script:

python model.py

This will regenerate bangalore_home_prices_model.pickle and columns.json.